It is the primary responsibility of a gas pipeline company to satisfy the gas demand of customers and ensure the economical operation of its pipelines. This paper presents a stochastic multi-objective transient peak shaving optimization model that considers uncertainty in gas demand while optimizing operational schemes of compressors and peak shaving measures of underground gas storages. The model is a rigorous multi-objective non-linear program (NLP) constrained by governing gas flow equations, operating envelopes of centrifugal compressors, and thermodynamic equations that model natural gas properties. The proposed multi-objective model aims to minimize the operational costs (the sum of the electricity cost of compressor stations and the gas withdrawal cost of underground gas storage) and maximize the line pack at the end of the time horizon subject to multiple legal and physical constraints. The model was tested on a real gas pipeline system, and a set of Pareto optimal solutions are obtained. The optimal operational schemes and peak shaving measures under three typical Pareto optimal solutions are analyzed in detail, and numerical results are presented following stochastic and robust optimization approaches.

天然气管道公司的主要责任是满足客户的天然气需求，并确保其管道的经济运行。本文提出了一种随机的多目标瞬态调峰优化模型，该模型考虑了天然气需求的不确定性，同时优化了压缩机的运行方案和地下储气库的调峰措施。该模型是严格的多目标非线性程序（NLP），受控制气流方程，离心压缩机的运行包络以及对天然气属性进行建模的热力学方程约束。拟议的多目标模型旨在使运营成本（压缩机站的电费和地下储气库的取气费之和）最小化，并在受多种法律和实务约束的情况下，在时间范围尽头使线路板最大化约束。该模型在真实的天然气管道系统上进行了测试，并获得了一组帕累托最优解。详细分析了三种典型的帕累托最优解下的最优操作方案和削峰措施，并通过随机和鲁棒的优化方法给出了数值结果。